Color Fidelity Research Articles

Vector light field display based on an intertwined flat lens with large depth of focus

The depth of focus (DOF) of a lens is a crucial parameter in glasses-free 3D displays that affects the viewing distance range. Here we proposed a vector light field display based on intertwined flat lens for extended viewing distance. The gray-scale diffractive lens (GDL) is designed and fabricated with extended DOF for red (658 nm), green (532 nm), and blue (450 nm) colors. By integrating the intertwined GDLs with a liquid crystal display, four views form a smooth horizontal parallax with a cross talk below 26% over a viewing distance from 24 to 90 cm. The enhancement of the DOF is 1.8 × 1 0 4 -fold. The light efficiency of the pixelated GDL reaches 82%. The proposed vector light field 3D display has the advantages of a thin form factor, high efficiency, high color fidelity, and large viewing distance. The potential applications include portable electronics, 3D TVs, and tabletop displays.

Ignition measurement of non-premixed propane with varying co-flowing AIR through high-speed schlieren stereoscopic colour imaging

The flame kernel propagation characteristics stemmed from the spark ignition of low-Re, non-premixed propane (C3H8) under varying air co-flow conditions were studied using DFCD (Digital Flame Colour Discrimination) incorporated stereoscopic image processing technique. The current work focused on the viability of obtaining the combustion initiating kernel propagation speed (SP), equivalence ratio (Φ) and kernel depth (z) characteristics simultaneously as these parameters are important in the fundamental combustion theories and highly desired in the field of practical burner monitoring and diagnostics. Through the superimposition of schlieren and DFCD-enhanced colour images, the observed spatial location of kernel spearhead was found to conform to the disturbance front observed from the schlieren visualisation. Flame stretch principle was applied to experimentally determine the un-stretched kernel propagation speeds (SP,O). Concurrently, the estimation of the temporal state of reactant mixture equivalence ratio (Φe) variation was made using DFCD-segregated spatial colour signal characteristics. Further comparison were made against the CHEMKIN simulated results of un-stretched laminar flame speed (SL,O) at different Φ. The experimentally determined SP,O values were found to overlap the SL,O range corresponding to the measured Φe. The steady occurrence of digital colour fidelity observed for all considered cases suggest that the most reactive mixture fraction (ξMR) path followed by the kernel occurred in the fuel-rich premixed Φ condition (approximately Φe = 1.34 to 1.40). Furthermore, stereoscopic reconstruction of DFCD-enhanced flame colour images provided additional dimensional linkage to correlate the dimensional-spectral variation along with changes in combustion flow conditions. The dimensional-spectral property, through the computation of local colour ratio and stereoscopically reconstructed z, illustrated linearity in both the time domain and in temporal cross-correlation with Φe. The observed shift of the cross-correlated cluster towards the fuel-richer Φe condition is coherent to the corresponding increase in Re and exemplifies the observed conformance of the derived SL,O and Φe to properties of premixed combustion along the propagation front potentially denoting the path of ξMR.

The study and analysis of using CMY color filter arrays for 0.8 um CMOS image sensors

Digital color cameras detect scenes through color filter array (CFA) of mosaic patterns. Among existing filter arrays in commercial CMOS image sensors (CIS), the CMY color filter set is one that is a more desirable choice for low-illuminating conditions. However, these color filters generally suffered from lower color fidelity than their counterpart RGB color filters. Nevertheless, the overall CIS performance were affected not only by the pigments used for color filters but also by the detailed structure designs and CFA arrangement of the pixel itself, we tends to explore the best combination for both the color filter materials and the pixel designs that will improve CIS performance for low light conditions that will have applications in astrophotography, low phototoxicity bioimaging, as well as general photography during dawn and dusk.

Learn to Be Clear and Colorful: An End-to-End Network for Panchromatic Image Enhancement

Benefiting from the high coverage and re-visiting frequency, the satellite imagery is an ideal data for large-scale, real-time earth observation. However, due to the limited resolution and chromatic information, the satellite images, especially the panchromatic images, are not capable of being used for accurate earth observations, such as road extraction, vehicle detection, and building segmentation. In this research, we propose a cascaded fully convolutional network (CFCN) consists of a residual dense super-resolution network (RDSRN) for grayscale image super-resolution (SR) and a residual deconvolution colorization network (RDCN) for grayscale image colorization. The unique architecture can simultaneously learn texture detail and color information from aerial images and then transfer to enhance panchromatic images. Furthermore, we also introduce an indirect evaluation metric, learned extraction similarity (LES), to estimate the image quality of the generated image in the absence of the ground truth. Experiments on a multispectral image dataset demonstrate that panchromatic images enhanced by the proposed CFCN are with both texture and color fidelity as compared to aerial image. For pre-trained U-Net, compared to the performance on raw panchromatic images, the CFCN enhanced images increase 12.8% of LES of overall accuracy (96.4% versus 83.6%).

AED-Net: A Single Image Dehazing

In the past decade, significant research effort has been directed toward developing single-image dehazing algorithms. Despite this effort, dehazing continues to present a challenge, particularly in complex real-world cases. Indeed, it is an ill-posed problem because scene transmission depends on unknown and nonhomogeneous depth information. This paper proposes a novel end-to-end adaptive enhancement dehazing network (AED-Net) to recover clean scenes from hazy images. We evaluate it quantitatively and qualitatively against several state-of-the-art methods on three commonly used dehazing benchmark datasets as well as hazy real-world images. Moreover, we evaluated it against the top-scoring methods of the Codalab NTIRE 2021 competition based on the dehazing challenge dataset. Extensive computer simulations demonstrated that AED-Net outperforms state-of-the-art single-image haze removal algorithms in terms of PSNR, SSIM, and other key metrics. Furthermore, it improves image texture, detail edges, boosts image contrast and color fidelity. Finally, AED-Net is more effective under complex real-world conditions.

Human Perceptual Quality Driven Underwater Image Enhancement Framework

Underwater images suffer from severe color casts, low contrast, and blurriness, which greatly degrade the visibility and color fidelity of underwater images. Recently, numerous underwater image enhancement (UIE) algorithms have been proposed. Existing synthetic datasets-based deep learning methods employ synthetic datasets to train UIE models. However, there is a gap between synthetic datasets and real underwater images, leading to poor generalization of synthetic datasets-based UIE methods. Besides, existing real datasets-based deep learning methods largely focus on minimizing the mean squared reconstruction error between UIE results and corresponding ground-truth on the real datasets, but do not take human visual perception into account. Thus, although they achieve high PSNR between UIE results and corresponding ground-truth obtained by user study on the real datasets, they often achieve unsatisfactory perceptual quality. To address these problems, we propose a Human Perceptual Quality Driven Underwater Image Enhancement Framework (HPQ-UIEF) to achieve better results in human perceptual quality and maintain satisfactory PSNR, which is trained on a real underwater enhancement quality assessment database (UEQAB). Specifically, an Underwater Image Quality Assessment Network (UIQAN) for UIE images is first proposed to assist UIE task, in which a novel depth map prior spatial attention block (DPPAB) is embedded into UIQAN. The DPPAB can adaptively recalibrate the quality-aware feature maps and model human visual attention in a data-driven manner. Then, the UIE model is proposed, in which the UIQAN is introduced as the loss function to optimize our UIE model in the direction of perceptual metrics. Moreover, since the confidence map acquired by UIQAN can effectively reflect the sensitivity of human perceptual of local area in an UIE image, the confidence map is introduced to our UIEF to help our UIEF to perceive the perceptually important regions. Thus, the confidence map is down-sampled and then concatenated into the decoder module of the UIE model, which can further improve the perceptual quality of the UIE results. Extensive experimental results show that the proposed HPQ-UIEF outperforms state-of-the-art UIE methods qualitatively and quantitatively.

A Color Cast Image Enhancement Method Based on Affine Transform in Poor Visible Conditions

In this letter, a simple yet effective dehazing framework is proposed, which consists of a novel color correction and a contrast enhancement. Most of the existing dehazing works focus on enhancing the contrast of the degraded images, but rarely of them concern about the color cast, which is ubiquitous in the scattering medium. To address the color distortion, an affine transform model-based color correction method is first proposed to improve the appearance of the image while preserving the details, which is inspired by the traditional color transfer. The color transfer alters the color values of a source image by sharing the global color statistics of a reference image, which makes it unsuitable to address the locally variable color deviations encountered in highly color distorted images as in poor visibility conditions (sandstorms and underwater). To alter color correction locally, we add local color fidelity and gradient constraint to the proposed technique, which overcomes the limitation that the traditional method depends too much on the global color statistics of the reference image and encourages it to handle the degraded image with various color casts and light conditions. In addition, a multiscale gradient-domain processing is applied to enhance the contrast. In this procedure, by extracting the information of different layers, we can easily restore the contrast while limiting the significant amplification of noise. The extensive qualitative and quantitative experiments reveal that the color and the contrast can be significantly improved by the proposed technique.

Research on Multi-modal Medical Image Fusion Based on Multi-Scale Attention Mechanism

Image fusion technology has been widely used and researched in the fields of medicine, aviation, military and civil. Compared with the traditional image fusion technology, the fusion image technology based on intelligent algorithm generates more realistic, clear and reliable images, and has a lot of detailed information. Deep neural networks have rapidly developed into a research hotspot in medical image analysis, which can automatically extract hidden pathological information from medical image data. Multi-scale and attention mechanism are two important modules in neural networks, which can significantly enhance the features extracted by the network. The problems that need to be solved for medical image fusion based on multi-scale attention mechanism include: how to build a multi-scale module, how to build an attention module, and how to combine multi-scale and attention mechanisms. Construct a deep learning network through a multi-scale attention mechanism, and then adjust the parameters and train the network to achieve multi-modal medical image fusion. In the process of building neural networks, multi-scale and attention mechanisms are incorporated, and multi-scale features of multi-modal medical images are extracted and enhanced for fusion. Through a large number of experiments: (1) The edge strength of the fused image is expected to increase by 10%-20% based on the average value of the existing algorithm; (2) The fused image can achieve high color fidelity and rich detailed information; (3) ) The time required for the fusion algorithm is expected to be reduced by 1%-10% based on the average value of the existing algorithm.

Contrast Enhancement of Colour Images by Optimized Fuzzy Intensification

Contrast enhancement is a critical and difficult issue because inappropriate enhancement by existing global image enhancement techniques might result in over or under enhancement. Varying areas of the image that are lighted indicate different shades and contrast in the output images. Projected technique uses local colour correction in the Hue Saturation Luminance (HSL) colour space. To control colour fidelity in initial phase an optimized fuzzy intensification parameters are extracted automatically form fuzzy inference system for that particular image. Finally optimized Fuzzy Intensification parameter constants are used to minimize overexposed and underexposed areas and offers elevated contrast improvement. Several lab test conducted to analyze the effectiveness of the proposed method with existing strategies. Many quality evaluation parameters are evaluated, and findings are compared to some known colour picture contrast enhancement approaches. The produced output comparatively better than many existing techniques which support a moderate measure to visual perception of the processed images.

Developing test color samples to compute color fidelity of light sources for printing matter

Based on the clustering optimization of test color samples and a psychophysical experiment, the objective and subjective color fidelity of light sources for printing matter is evaluated, and an improved evaluation method thus has been proposed. Firstly, for representing the output characteristics of printing press, the International Color Consortium standard color target samples (ICC SCTS), which is measured in the process of color management, is used as a large color sample set. In a 6D spectral color space proposed in this paper, the optimized color sample set (OCSS) is obtained by Self Organizing Maps Neural Networks (SOMNN) clustering algorithm from ICC SCTS. Taking OCSS, ICC SCTS, and standard color sample set (SCSS) as the test color sample set, three objective color fidelity indexes (CFIs) CIE-Ra, Ra,2012, and CIE-Rf of 1202 light sources are calculated. The correlation metrics of the CFIs show that the OCSS highly improved the objective accuracy of color fidelity evaluation for printing matters. Secondly, in the psychophysical experiment, 20 observers have evaluated the visual color difference of the OCSS under the illumination of nine pairs of test and reference light sources. The subjective CFIs are calculated by using the visual color difference of OCSS obtained from the psychophysical experiment. In order to improve the subjective and objective consistency of CFIs, a polynomial modified model for objective color difference of OCSS is proposed. By the optimization of test color samples and the modification of color difference calculation, the method developed in this paper can be effectively and conveniently applied to the subjective and objective evaluation of light source for printing matters.

Optimising metameric spectra for integrative lighting to modulate the circadian system without affecting visual appearance

Smart integrative lighting systems aim to support human health and wellbeing by capitalising on the light-induced effects on circadian rhythms, sleep, and cognitive functions, while optimising the light’s visual aspects like colour fidelity, visual comfort, visual preference, and visibility. Metameric spectral tuning could be an instrument to solve potential conflicts between the visual preferences of users with respect to illuminance and chromaticity and the circadian consequences of the light exposure, as metamers can selectively modulate melanopsin-based photoreception without affecting visual properties such as chromaticity or illuminance. This work uses a 6-, 8- and 11-channel LED luminaire with fixed illuminance of 250 lx to systematically investigate the metameric tuning range in melanopic equivalent daylight illuminance (EDI) and melanopic daylight efficacy ratio (melanopic DER) for 561 chromaticity coordinates as optimisation targets (2700 K to 7443 K ± Duv 0 to 0.048), while applying colour fidelity index Rf criteria from the TM-30-20 Annex E recommendations (i.e. Rfge 85, Rf,h1ge 85). Our results reveal that the melanopic tuning range increases with rising CCT to a maximum tuning range in melanopic DER of 0.24 (CCT: 6702 K, Duv: 0.003), 0.29 (CCT: 7443 K, Duv: 0) and 0.30 (CCT: 6702, Duv: 0.006), depending on the luminaire’s channel number of 6, 8 or 11, respectively. This allows to vary the melanopic EDI from 212.5–227.5 lx up to 275–300 lx without changes in the photopic illuminance (250 lx) or chromaticity (Delta u'v'le 0.0014). The highest metameric melanopic Michelson contrast for the 6-, 8- and 11-channel luminaire is 0.16, 0.18 and 0.18, which is accomplished at a CCT of 3017 K (Duv: − 0.018), 3456 K (Duv: 0.009) and 3456 K (Duv: 0.009), respectively. By optimising ~ 490,000 multi-channel LED spectra, we identified chromaticity regions in the CIExy colour space that are of particular interest to control the melanopic efficacy with metameric spectral tuning.

Underwater image restoration by color compensation and color-line model

The images captured underwater suffer from low contrast and color distortion owing to the harsh underwater imaging environment. An underwater image restoration algorithm is introduced in this paper based on color compensation and color-line model. Firstly, color compensation method is proposed to compensate the attenuated color information and improve the accuracy of estimated color lines. Secondly, the relationship of three-channel transmission is derived from the association between the global background light and the inherent optical characteristics. Then, an underwater image local transmission optimization model is established in light of the color line law to acquire local transmission map. Finally, the restoration images are performed by inverting the form of the simplified imaging model. Experimental results show that our method is superior to other methods in subjective evaluation, objective evaluation, color accuracy tests and application tests. This method can obtain images with significantly enhanced visibility and higher color fidelity.

Analysis of chromatic aberrations influence on operation of the tunable AOTF-based source

Developing a source with the possibility of tuning in wavelengths based on an acousto-optical tunable filter (AOTF), an essential factor is the ability to provide accurate color coordinates of the simulated color (in the case of developing a color standard for colorimetry) or the required wavelength (in the case of an application for spectral studies). As shown earlier, the choice of the principal layout – confocal or parallel beam path - primarily determines the dimensions and efficiency of using the luminous flux. However, these schemes also need to be analyzed for color or wavelength fidelity, considering other components used in the scheme. The analysis performed allows to identify the optimal scheme to ensure the required color reproduction accuracy and establish the requirements for correcting chromatic aberrations of the components.

White Balance preference under multiple light sources

White balance is one of the key processes in a camera pipeline. Accuracy can be challenging when a scene is illuminated by multiple color light sources. We designed and built a studio which consisted of a controllable multiple LED light sources that produced a range of correlated color temperatures (CCTs) with high color fidelity that were used to illuminate test scenes. A two Alternative Forced Choice (2AFC) experiment was performed to evaluate the white balance appearance preference for images containing a model in the foreground and target objects in the background indoor scene. The foreground and background were lit by different combinations of cool to warm sources. The observers were asked to pick the one that was most aesthetically appealing to them. The results show that when the background is warm, the skin tones dominated observers' decisions and when the background is cool the preference shifts to scenes with same foreground and background CCT. The familiarity and unfamiliarity of objects in the background scene did not show a significant effect.

Fabrication of Circularly Polarized MR-TADF Emitters with Asymmetrical Peripheral-Lock Enhancing Helical B/N-Doped Nanographenes.

Circularly polarized thermally activated delayed fluorescence (CP-TADF) and multiple-resonance thermally activated delayed fluorescence (MR-TADF), which exhibit novel circularly polarized luminescence and excellent color fidelity, respectively, have gained immense popularity. In this study, integrated CP-TADF and MR-TADF (CPMR-TADF) are prepared by strategic design and synthesis of asymmetrical peripherally locked enantiomers, which are separated and denoted as (P,P″,P″)-/(M,M″,M″)-BN4 and (P,P″,P″)-/(M,M″,M″)-BN5 and exhibit TADF and circularly polarized light (CPL) properties. As the entire molecular frame participates in the frontier molecular orbitals, the resulting helical chirality of (+)/(-)-BN4- and (+)/(-)-BN5-based solution-processed organic light-emitting diodes (OLEDs) helps in achieving a narrow full width at half maximum (FWHM) of 49/49 and 48/48nm and a high maximum external quantum efficiency (EQE) of 20.6%/19.0% and 22.0%/26.5%, respectively. Importantly, unambiguous circularly polarized electroluminescence signals with dissymmetry factors (gEL ) of +3.7 × 10-3 /-3.1 × 10-3 (BN4) and +1.9 × 10-3 /-1.6 × 10-3 (BN5) are obtained. The results indicate successful exploitation of CPMR-TADF-emitter-based OLEDs to exhibit three characteristics: high efficiency, color purity, and circularly polarized light.

New strategy for high-dimensional single-pixel imaging.

Single-pixel imaging (SPI) technique has been studied intensively due to its minimum requirement for the detector resolution and the equipment costs. In this work, we proposed a new strategy of the SPI to explore its capability in high-dimensional imaging, which is the first comprehensive scheme as we know to achieve calibration, color texture and viewpoint expansion of single-pixel three-dimensional imaging. We realized a low-cost single-pixel three-dimensional imaging scheme which employ a raster scanner to provide the structured illumination and a grating to encode the height information. In order to reduce the blocking area, we introduce two single-pixel detectors (SPDs) to detect from two detection angles, a modified total variation based criterion is proposed to fuse the height information from two SPDs and reduce the error of shape fusion. To acquire the information of higher dimension, we introduce the third SPD aims to gain the color texture, three bandpass filter is placed in front of three SPDs, respectively, to collect different color information. Meanwhile a viewpoint switching method inspired by the shape from shading theory is presented to improve the color fidelity. Our study is expected to provide a demonstration for SPI in acquisition, reconstruction, and fusion of high-dimensional image data.

Content Fidelity of Deep Learning Methods for Clipping and Over-exposure Correction

Exposure problems, due to standard camera sensor limitations, often lead to image quality degradations such as loss of details and change in color appearance. The quality degradations further hiders the performances of imaging and computer vision applications. Therefore, the reconstruction and enhancement of uderand over-exposed images is essential for various applications. Accordingly, an increasing number of conventional and deep learning reconstruction approaches have been introduced in recent years. Most conventional methods follow color imaging pipeline, which strongly emphasize on the reconstructed color and content accuracy. The deep learning (DL) approaches have conversely shown stronger capability on recovering lost details. However, the design of most DL architectures and objective functions don’t take color fidelity into consideration and, hence, the analysis of existing DL methods with respect to color and content fidelity will be pertinent. Accordingly, this work presents performance evaluation and results of recent DL based overexposure reconstruction solutions. For the evaluation, various datasets from related research domains were merged and two generative adversarial networks (GAN) based models were additionally adopted for tone mapping application scenario. Overall results show various limitations, mainly for severely over-exposed contents, and a promising potential for DL approaches, GAN, to reconstruct details and appearance.

Laryngeal High-Speed Videoendoscopy with Laser Illumination: A Preliminary Report.

<br><b>Introduction:</b> Advances in computer image analysis have enabled the use of new functional imaging methods in the diagnosis of laryngeal diseases. Particularly interesting techniques of dynamic laryngeal imaging involve High Speed Videoendoscopy (HSV). This still-developed technique allows to overcome the limitations of laryngovideostroboscopy (LVS) and a more detailed analysis of the glottal function based on the image of the actual vibrations of the vocal folds. It also enables the determination of objective coefficients parameterizing phonatory vibrations of the vocal folds.</br> <br><b>Aim:</b> The aim of this pilot study was to evaluate the use of a high-speed videoendoscopy set with laser illumination for the diagnosis of glottic pathology in ENT practice.</br> <br><b>Material and methods:</b> The study included 40 patients who underwent LVS followed by HSV. The modern HSV examination kit - Advanced Larynx Imager System (ALIS), used for the first time in a clinical setting in Poland, is characterized by significantly improved, compared to the previously used high-speed cameras, operational parameters - a light head, the possibility of continuous lighting operation without excessive heating of the head tip, registration of the image in full color scale. Thanks to such modernization, the safety and course of the examination do not differ from laryngoscopy conducted with commonly used recorders. The device owes some of these improvements to a laser illuminator which was used for the first time as the main light source in a high-speed camera. In the study, two cases were selected to present the results of HSV and the analysis of the generated kymograms - a woman with no glottic pathology and a man with a polyp of the right vocal fold. In the first case, the HSV examination compared with the LVS revealed a discrete glottis functional disorder in the form of a tendency to hyperphonation. The patient with an organic lesion had a clearly visible irregularity of vocal fold vibrations, which also allowed to trace mucosal wave disturbances related to its reflection from the pathological structure of the glottis and the formation of a return wave, both on the fold affected by the lesion and, to a lesser extent, contralaterally. The glottic dysfunctions observed in the studied patients were confirmed in the generated kymograms and the graphs of the glottal width waveform (GWW), as well as in the parameters calculated on their basis, assessing the frequency and amplitude of phonatory vibrations.</br> <br><b>Conclusions:</b> The use of high-speed videoendoscopy allows for a much more accurate assessment of the phonatory function of the glottis than in laryngovideostroboscopy. The presented HSV system allows for obtaining high quality kinematic images of the larynx, color fidelity, and contrast. The use of this technology in laryngological practice enables precise structural and functional assessment of the glottis and detection of discrete phonation disorders that elude the techniques used so far.</br&gt.

Optimization of the dynamic light source considering human age effect on visual and non-visual performances

The spectral transmittance of ocular media on the short-wavelength light is higher for young observers than elders. It is essential to concentrate on the photobiological safety of children and circadian effect of elders when designing the light sources. In this study, we optimized the peak wavelengths and ratios of four-channel light emitting-diodes (LEDs), to fabricate the dynamic white light which performed well on photobiological safety, circadian effect, and color rendition for observers of different ages. Based on the optimal results, we selected the four monochromatic LEDs of 461.0, 525.0, 589.0, and 660.0 nm, and then fabricated the practical white light. The correlated color temperature (CCT) of the dynamic white light is tunable from 2700 to 6500 K. For 1-year-old observers, the blue light hazard efficiency of radiation (BLHER) is less than 0.237. For 70-year-old observers, the melanopic efficacy of luminous radiation (MELR) reaches 0.987 mW/lm and its tunability is optimized to be 2.10. The general color rendering index (Ra), special color rendering index (R9), color fidelity index (Rf), and color gamut index (Rg) are more than 89.3, 79.1,81.7, and 93.6, respectively. At last, we also discussed the impacts of the peak wavelength variation on the performances of hybrid white light.

SAR-to-optical image translation based on improved CGAN

SAR images have the advantages of being less susceptible to clouds and light, while optical images conform to the human vision system. Both of them are widely applied in the field of scene classification, natural environment monitoring, disaster warning, etc. However, due to the speckle noise caused by the SAR imaging principle, it is difficult for people to distinguish the ground objects from complex background without professional knowledge. One commonly used solution is to exploit Generative Adversarial Networks (GAN) to translate SAR images to optical images which is able to clearly present ground objects with rich color information, i.e., SAR-to-optical image translation. Traditional GAN-based translation methods are apt to cause blurring of contour, disappearance of texture and inconsistency of color. To this end, we propose an improved conditional GAN (ICGAN) method. Compared with the basic CGAN model, the translation ability of our method is improved in the following three aspects. (1) Contour sharpness. We utilize the parallel branches to combine low-level and high-level features, and thus the image contour information is improved without the influence of noise. (2) Texture fine-grainedness. We discriminate the image using multi-scale receptive fields to enrich the local and global texture features of the image. (3) Color fidelity. We use the chromatic aberration loss which is based on Gaussian blur convolution to reduce the color gap between the generated image and the real optical image. Our method considers both the visual layer and the conceptual layer of the image to complete the SAR-to-optical image translation task. The model is able to preserve the contours and textures of the SAR image, while more closely approximates the colors of the ground truth. The experimental results show that the generated image not only has preferable results in visual effects and favorable evaluation metrics (subjective and objective), but also achieves outstanding classification accuracy, which proves the superiority of our method over the state-of-the-arts in the SAR-to-optical image translation task.